Decision making is one of the most critical functions of the human brain, impacting all aspects of our life on time scales extending from tenths of seconds to days. Over the past decade, experimental studies have begun to reveal the neural mechanisms that underlie simple decisions, such as deciding whether an object is moving to right or to the left. For binary decisions about motion perception, the brain's strategy is consistent with a class of race (or diffusion) models: evidence is integrated to form a decision variable, which terminates the process when it reaches a criterion level. In parallel to this work, several groups have developed neural theories of optimal Bayesian inference, focusing more particularly on how neurons represent probability distributions and how they update these distributions over time in a Bayes optimal way. The goal of this proposal is to bring together this theoretical work on optimal Bayesian inference with the experimental data on decision making. We propose an interactive program of theoretical and experimental studies to examine the validity and limitations of a theoretical framework known as probabilistic population codes (or PPC for short). Our studies combine modeling of existing data and design of new experiments that test this PPC theories. We will first develop a neural network model with integrate-and-fire spiking neurons for decision making over a continuous variable (direction of motion) using PPCs. Next, we will simulate the model in a psychophysics experiment to determine its performance and reaction time when tested on N-forced-choice motion discrimination in which the model has to decide between N4, 8 or an infinite number of directions. These predictions will then be compared with the performance on monkeys and humans in the same experiments. Finally, we will use the model to generate predictions about the response of LIP neurons which will then be tested through single and multielectrode recordings while monkeys perform a motion discrimination task. We will also consider an alternative class of models for Bayesian inference, which we call the log probability model. This contrasting hypothesis lies at the heart of several recent studies of Bayesian inference in neural circuits. Understanding the brain mechanisms of decision making will ultimately benefit patients with mental and neurological disorders affecting diverse cognitive functions such as demential, neglect, apraxia and addiction. [unreadable] [unreadable] [unreadable]